Anye DEN WARREN Portfolio

👤 Anye Den Warren is an Electrical Engineer and emerging Data Scientist with a passion for building technology solutions for public institutions, NGOs, and social-impact organizations across Africa. His work blends machine learning, signal processing, and policy insights to address real-world challenges.
linkedin: @anyeigwacho
Medium: Blog on African Public Policy
ResearchGate: Articles and Projects.
Founder of Reastech: Reastech

Classification Problem in Matlab

In this project we implemented multiple Machine learning models to classify MRI images under Healthy, Mild cognitive impairment and dementia, we obtained 86.7% accuracy for SVM,86.7% for Naïve Bayes, 75% for KNN and 86.7% for Random Forest

April 24, 2022

Data Analysis in Excel and Matlab

In this project we looked at the effect of GDP growth on population growth of 24 African countries divided among 3 subregions namely East Africa,West Africa and Southern Africa

April 22, 2020

Pan Tompkins Algorithm in Matlab

In this project,we implemented the famous Pan-Tompkins algorithm on multiple Electrocardiogram(ECG) signals(ECG 4, ECG 5, ECG 6), In other to determine the Total Number of heartbeats, Beats per mins, Average RR Interval, Standard Deviation of RR Interval, Average QRS width.

April 18, 2022

Linear Regression Problem in Matlab

In this project we investigated the impact of foreign direct investment(FDI) on macroeconomic development factors(National income and GDP per capita) in V4 countries(Czech Republic,Hungary,Poland and Slovakia).

April 11, 2022

Regularized Linear Regression and Support Vector Machine

In this report we implemented the following machine learning algorithm on biomedical data sets: feed-forward neural network using backpropagation on the fisher iris data set, regularized linear regression and bias variance and SVM in determining the normal, elevated and stage1 high blood pressure of a patient give the systolic and diastolic blood pressures.

April 7, 2020

Image Processing in Matlab and Python

In this project we implemented the following filtering and processing techniques:Negative transformation, Log transformation, Power-law transformation, Contrast stretching, Dynamic Range expansion, Intensity level slicing (gray level slicing), Bit plane splicing and filtering with Edge kernel .